Behavioral recognition system

ABSTRACT

Embodiments of the present invention provide a method and a system for analyzing and learning behavior based on an acquired stream of video frames. Objects depicted in the stream are determined based on an analysis of the video frames. Each object may have a corresponding search model used to track an object&#39;s motion frame-to-frame. Classes of the objects are determined and semantic representations of the objects are generated. The semantic representations are used to determine objects&#39; behaviors and to learn about behaviors occurring in an environment depicted by the acquired video streams. This way, the system learns rapidly and in real-time normal and abnormal behaviors for any environment by analyzing movements or activities or absence of such in the environment and identifies and predicts abnormal and suspicious behavior based on what has been learned.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.12/028,484, filed Feb. 8, 2008, now U.S. Pat. No. 8,131,012, whichclaims benefit of United States provisional patent application Ser. No.60/888,777, filed Feb. 8, 2007. Each of the aforementioned relatedpatent applications is herein incorporated by reference in its entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention generally relates to video analysis, and moreparticularly to analyzing and learning behavior based on streaming videodata.

2. Description of the Related Art

Some currently available video surveillance systems have simplerecognition capabilities. However, many such surveillance systemsrequire advance knowledge (before a system has been developed) of theactions and/or objects the systems have to be able to seek out.Underlying application code directed to specific “abnormal” behaviorsmust be developed to make these surveillance systems operable andsufficiently functional. In other words, unless the system underlyingcode includes descriptions of certain behaviors, the system will beincapable of recognizing such behaviors. Further, for distinctbehaviors, separate software products often need to be developed. Thismakes the surveillance systems with recognition capabilities laborintensive and prohibitively costly. For example, monitoring airportentrances for lurking criminals and identifying swimmers who are notmoving in a pool are two distinct situations, and therefore may requiredeveloping two distinct software products having their respective“abnormal” behaviors pre-coded.

The surveillance systems may also be designed to memorize normal scenesand generate an alarm whenever what is considered normal changes.However, these types of surveillance systems must be pre-programmed toknow how much change is abnormal. Further, such systems cannotaccurately characterize what has actually occurred. Rather, thesesystems determine that something previously considered “normal” haschanged. Thus, products developed in such a manner are configured todetect only a limited range of predefined type of behavior.

SUMMARY OF THE INVENTION

Embodiments of the present invention provide a method and a system foranalyzing and learning behavior based on an acquired stream of videoframes. Objects depicted in the stream are determined based on ananalysis of the video frames. Each object may have a correspondingsearch model, which are used to track objects' motions frame-to-frame.Classes of the objects are determined and semantic representations ofthe objects are generated. The semantic representations are used todetermine objects' behaviors and to learn about behaviors occurring inan environment depicted by the acquired video streams. This way, thesystem learns rapidly and in real-time normal and abnormal behaviors forany environment by analyzing movements or activities or absence of suchin the environment and identifies and predicts abnormal and suspiciousbehavior based on what has been learned.

One particular embodiment of the invention includes a method forprocessing a stream of video frames recording events within a scene. Themethod may generally include receiving a first frame of the stream. Thefirst frame includes data for a plurality of pixels included in theframe. The method may further include identifying one or more groups ofpixels in the first frame. Each group depicts an object within thescene. The method may still further include generating a search modelstoring one or more features associated with each identified object,classifying each of the objects using a trained classifier, tracking, ina second frame, each of the objects identified in the first frame usingthe search model, and supplying the first frame, the second frame, andthe object classifications to a machine learning engine. The method maystill further include generating, by the machine learning engine, one ormore semantic representations of behavior engaged in by the objects inthe scene over a plurality of frames. The machine learning engine maygenerally be configured to learn patterns of behavior observed in thescene over the plurality of frames and to identify occurrences of thepatterns of behavior engaged in by the classified objects.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features, advantages andobjects of the present invention are attained and can be understood indetail, a more particular description of the invention, brieflysummarized above, may be had by reference to the embodiments illustratedin the appended drawings.

It is to be noted, however, that the appended drawings illustrate onlytypical embodiments of this invention and are therefore not to beconsidered limiting of its scope, for the invention may admit to otherequally effective embodiments.

FIG. 1 is a high-level block diagram of a behavior recognition system,according to one embodiment of the present invention.

FIG. 2 illustrates a flowchart of a method for analyzing and learningbehavior based on a stream of video frames, according to one embodimentof the present invention.

FIG. 3 illustrates a background-foreground module of a computer visionengine, according to one embodiment of the present invention.

FIG. 4 illustrates a module for tracking objects of interest in acomputer vision engine, according to one embodiment of the presentinvention.

FIG. 5 illustrates an estimator/identifier module of a computer visionengine, according to one embodiment of the present invention.

FIG. 6 illustrates a context processor component of a computer visionengine, according to one embodiment of the present invention.

FIG. 7 illustrates a semantic analysis module of a machine learningengine, according to one embodiment of the present invention.

FIG. 8 illustrates a perception module of a machine learning engine,according to one embodiment of the present invention.

FIGS. 9A-9C illustrate a sequence of a video frames where a behaviorrecognition system detects an abnormal behavior and issues an alert,according to one embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Machine-learning behavior-recognition systems, such as embodiments ofthe invention described herein, learn behaviors based on informationacquired over time. In context of the present invention, informationfrom a video stream (i.e., a sequence of individual video frames) isanalyzed. This disclosure describes a behavior recognition system thatlearns to identify and distinguish between normal and abnormal behaviorwithin a scene by analyzing movements and/or activities (or absence ofsuch) over time. Normal/abnormal behaviors are not pre-defined orhard-coded. Instead, the behavior recognition system described hereinrapidly learns what is “normal” for any environment and identifiesabnormal and suspicious behavior based on what is learned throughmonitoring the location, i.e., by analyzing the content of recordedvideo frame-by-frame.

In the following, reference is made to embodiments of the invention.However, it should be understood that the invention is not limited toany specifically described embodiment. Instead, any combination of thefollowing features and elements, whether related to differentembodiments or not, is contemplated to implement and practice theinvention. Furthermore, in various embodiments the invention providesnumerous advantages over the prior art. However, although embodiments ofthe invention may achieve advantages over other possible solutionsand/or over the prior art, whether or not a particular advantage isachieved by a given embodiment is not limiting of the invention. Thus,the following aspects, features, embodiments and advantages are merelyillustrative and are not considered elements or limitations of theappended claims except where explicitly recited in a claim(s). Likewise,reference to “the invention” shall not be construed as a generalizationof any inventive subject matter disclosed herein and shall not beconsidered to be an element or limitation of the appended claims exceptwhere explicitly recited in a claim(s).

One embodiment of the invention is implemented as a program product foruse with a computer system. The program(s) of the program productdefines functions of the embodiments (including the methods describedherein) and can be contained on a variety of computer-readable storagemedia. Illustrative computer-readable storage media include, but are notlimited to: (i) non-writable storage media (e.g., read-only memorydevices within a computer such as CD-ROM disks readable by a CD-ROMdrive) on which information is permanently stored; (ii) writable storagemedia (e.g., floppy disks within a diskette drive or hard-disk drive) onwhich alterable information is stored. Such computer-readable storagemedia, when carrying computer-readable instructions that direct thefunctions of the present invention, are embodiments of the presentinvention. Other media include communications media through whichinformation is conveyed to a computer, such as through a computer ortelephone network, including wireless communications networks. Thelatter embodiment specifically includes transmitting information to andfrom the Internet and other networks. Such communications media, whencarrying computer-readable instructions that direct the functions of thepresent invention, are embodiments of the present invention. Broadly,computer-readable storage media and communications media may be referredto herein as computer-readable media.

In general, the routines executed to implement the embodiments of theinvention may be part of an operating system or a specific application,component, program, module, object, or sequence of instructions. Thecomputer program of the present invention is comprised typically of amultitude of instructions that will be translated by the native computerinto a machine-readable format and hence executable instructions. Also,programs are comprised of variables and data structures that eitherreside locally to the program or are found in memory or on storagedevices. In addition, various programs described herein may beidentified based upon the application for which they are implemented ina specific embodiment of the invention. However, it should beappreciated that any particular program nomenclature that follows isused merely for convenience, and thus the invention should not belimited to use solely in any specific application identified and/orimplied by such nomenclature.

Embodiments of the present invention provide a behavior recognitionsystem and a method for analyzing, learning, and recognizing behaviors.FIG. 1 is a high-level block diagram of the behavior recognition system100, according to one embodiment of the present invention. As shown, thebehavior recognition system 100 includes a video input 105, a network110, a computer system 115, and input and output devices 145 (e.g., amonitor, a keyboard, a mouse, a printer, and the like).

The network 110 receives video data (e.g., video stream(s), videoimages, or the like) from the video input 105. The video input 105 maybe a video camera, a VCR, DVR, DVD, computer, or the like. For example,the video input 105 may be a stationary video camera aimed at certainarea (e.g., a subway station) and continuously recording the area andevents taking place therein. Generally, the area visible to the camerais referred to as the “scene.” The video input 105 may be configured torecord the scene as a sequence of individual video frames at a specifiedframe-rate (e.g., 24 frames per second), where each frame includes afixed number of pixels (e.g., 320×240). Each pixel of each framespecifies a color value (e.g., an RGB value). Further, the video streammay be formatted using known such formats e.g., MPEG2, MJPEG, MPEG4,H.263, H.264, and the like. As discussed in greater detail below, thebehavior recognition system analyzes this raw information to identifyactive objects in the stream, classifies such elements, derives avariety of metadata regarding the actions and interactions of suchelements, and supplies this information to a machine learning engine. Inturn, the machine learning engine may be configured to evaluate, learn,and remember over time. Further, based on the “learning,” the machinelearning engine may identify certain behaviors as anomalous.

The network 110 may be used to transmit the video data recorded by thevideo input 105 to the computer system 115. In one embodiment, thenetwork 110 transmits the received stream of video frames to thecomputer system 115.

Illustratively, the computer system 115 includes a CPU 120, storage 125(e.g., a disk drive, optical disk drive, floppy disk drive, and thelike), and memory 130 containing a computer vision engine 135 andmachine learning engine 140. The computer vision engine 135 may providea software application configured to analyze a sequence of video framesprovided by video input 105. For example, in one embodiment, thecomputer vision engine 135 may be configured to analyze video frames toidentify targets of interest, track those targets of interest, inferproperties about the targets of interest, classify them by categories,and tag the observed data. In one embodiment, the computer vision engine135 generates a list of attributes (such as texture, color, and thelike) of the classified objects of interest and provides the list to themachine learning engine 140. Additionally, the computer vision enginemay supply the machine learning engine 140 with a variety of informationabout each tracked object within a scene (e.g., kinematic data, depthdata, color, data, appearance data, etc.).

The machine learning engine 140 receives the video frames and theresults generated by the computer vision engine 135. The machinelearning engine 140 analyzes the received data, builds semanticrepresentations of events depicted in the video frames, determinespatterns, and learns from these observed behaviors to identify normaland/or abnormal events. The computer vision engine 135 and the machinelearning engine 140 and their components are described in greater detailbelow. Data describing whether a normal/abnormal behavior/event has beendetermined and/or what such behavior/event is may be provided to anoutput devices 145 to issue alerts, for example, an alert messagepresented on a GUI interface screen.

In general, both the computer vision engine 135 and the machine learningengine 140 process the received video data in real-time. However, timescales for processing information by the computer vision engine 135 andthe machine learning engine 140 may differ. For example, in oneembodiment, the computer vision engine 135 processes the received videodata frame by frame, while the machine learning engine processes thereceived data every N-frames. In other words, while the computer visionengine 135 analyzes each frame in real-time to derive a set ofinformation about what is occurring within a given frame, the machinelearning engine 140 is not constrained by the real-time frame rate ofthe video input.

Note, however, FIG. 1 illustrates merely one possible arrangement of thebehavior recognition system 100. For example, while the video input 105is shown connected to the computer system 115 via the network 110, thenetwork 110 is not always present or needed (e.g., the video input 105may be directly connected to the computer system 115). Further, in oneembodiment, the computer vision engine 135 may be implemented as a partof a video input device (e.g., as a firmware component wired directlyinto a video camera). In such a case, the outputs of the video cameramay be provided to the machine learning engine 140 for analysis.

FIG. 2 illustrates a method 200 for analyzing and learning behavior froma stream of video frames, according to one embodiment of the presentinvention. As shown, the method 200 begins at step 205. At step 210, aset of video frames are received from a video input source. At step 215,the video frames may be processed to minimize video noise, irregular orunusual scene illumination, color-related problems, and so on. That is,the content of the video frames may be enhanced to improve visibility ofthe images prior to processing by components of a behavior recognitionsystem (e.g., the computer vision engine 135 and machine learning engine140 discussed above).

At step 220, each successive video frame is analyzed to identify and/orupdate a foreground and background image for use during subsequentstages of the method 200. In general, the background image includesstationary elements of the scene being captured by the video input(e.g., pixels depicting a platform of a subway station), while theforeground image includes volatile elements captured by the video input(e.g., pixels depicting a man moving around the platform). In otherwords, the background image provides a stage upon which foregroundelements may enter, interact with one another, and leave. The backgroundimage may include a color value for each pixel in the background image.In one embodiment, the background image may be derived by sampling colorvalues for a given pixel over number of frames. Also, as new frames arereceived, elements of the background image may be updated based onadditional information included in each successive frame. Typically,which pixels are parts of the background or foreground may be determinedfor each frame in a sequence of video frames, and foreground elementsmay be identified by comparing the background image with the pixel colorvalues in a given frame. Once the foreground pixels are identified, amask may be applied to the frame, effectively cutting pixels that arepart of the background from an image, leaving only one or more blobs offoreground pixels in the image. For example, masks could be applied to aframe such that each foreground pixel is represented as white and eachbackground pixel is represented as black. The resulting black and whiteimage (represented as a two-dimensional array) may be provided tosubsequent elements of the behavior recognition system. In oneembodiment, the computer system 115 may be provided with initial modelsof a background image for a given scene.

At step 225, a foreground image associated with a given frame may beanalyzed to identify a set of blobs (i.e., a group of related pixels) bysegmenting the foreground image into targets of interest. In otherwords, the system may be configured to isolate distinct blobs within theforeground image, where each blob is likely to represents a differentforeground object within the frame (e.g., a car, man, suitcase, and thelike). For each foreground blob, a search model may be initialized whena foreground blob is initially identified. The search model is used tocapture a position of a blob within the scene, identity which pixels areincluded as part of the blob, and store a variety of metadata regardingthe observed behavior of the blob from frame-to-frame. Further, thesearch model may be used by a tracking module to predict, find, andtrack motions of a corresponding object from frame-to-frame. Assuccessive frames are received, the search model is updated as theforeground blob continues to be present through successive video frames.Such updates may be performed with each additional video frame,periodically, as new information allows the refining of the search modelis received, as needed, or the like.

The search model may be implemented in a variety of ways. For example,in one embodiment, the search model may be an appearance modelconfigured to capture a number of features about a given foregroundobject, including which pixels are considered part of that foregroundobject. The appearance model of a given object may then be updated,based on the pixels representing that object from frame to frame. Inanother embodiment, the search model may be a minimal bounding rectangleto encompass an object. While computed more quickly, the minimallybounding rectangle includes pixels as part of the blob that are, infact, part of the background. Nevertheless, for some types of analysis,this approach may be effective. These search models are described belowin greater detail. At step 230, the search models are used to trackmotions of the foreground objects as they move about the scene fromframe-to-frame. That is, once an object is identified in a first frameand an appearance model (and/or bounding box) is generated for thatobject, the search model may be used to identify and track that objectin subsequent frames, based on the appearance model (and/or boundingbox), until that foreground object leaves the scene. The search modelmay be used to identify an object within the video frames after theobject, for example, changes location or position. Thus, different typesof information regarding the same objects are determined (e.g.,kinematic characteristics of the object, orientation, direction ofmovement, and so on) as such an object moves through the scene.

At step 235, the behavior recognition system attempts to classify theforeground blobs as being one of discrete number classifications. Forexample, in one embodiment, the behavior recognition system may beconfigured to classify each foreground object as being one of a “human,”a “vehicle,” an “other,” or an “unknown.” Of course, moreclassifications may be used and further, classifications may be tailoredto suit the needs of an individual case. For example, a behaviorrecognition system receiving video images of a luggage conveyer beltcould classify objects on the belt as different types/sizes of luggage.After classifying a foreground object, further estimations regardingsuch object may be made, e.g., the object's pose (e.g., orientation,posture, and the like), location (e.g., location within a scene depictedby the video images, location relative to other objects of interest, andlike), and motion (e.g., trajectory, speed, direction, and the like) areestimated. This information may be used by the machine learning engine140 to characterize certain behaviors as normal or anomalous, based onpast observations of similar objects (e.g., other objects classified ashumans).

At step 240, the results of previous steps (e.g., the tracking results,the background/foreground image data, the classification results, and soon) are combined and analyzed to create a map of a scene depicted by thevideo frames. In one embodiment, the scene is segmented into spatiallyseparated regions, each segment being defined by a set of pixels. Theregions are sorted according to z-depth (i.e., which segment is closerand which segment is further from a video capture device) and areoptionally labeled (e.g., as natural, man-made, etc.). At step 245,semantic representations of the objects' motions are created. In otherwords symbolic representations of the movements and/or actions of thetracked objects are created (e.g., “car parks,” “car stops,” “personbends,” “person disappears,” and so on). At step 250, the semanticrepresentations are analyzed for recognizable patterns.

The resulting semantic representations, annotated map of a scene, andthe classification results are analyzed at step 255. The behaviorrecognition system analyzes such results to learn patterns of behavior,generalizes based on observations, and learns by making analogies. Thisalso allows the behavior recognition system to determine and/or learnwhich kind of behavior is normal and which kind of behavior is abnormalThat is, the machine learning engine may be configured to identifyrecognizable patterns, evaluate new behaviors for a given object,reinforce or modify the patterns of behaviors learned about a givenobject, etc.

At step 260, the results of the previous steps are optionally analyzedfor recognized behavior. Additionally, the behavior recognition systemmay be configured to perform a specified action in response torecognizing the occurrence of a given event. For example, based on theresults of previous steps, the behavior recognition system may issue analert when a foreground object classified as a human engages in unusualbehavior. Further, whether some behavior is “unusual” may be based onwhat the learning engine has “learned” to be “normal” behavior forhumans in a given scene. In one embodiment, alerts are issued only if anabnormal behavior has been determined (e.g., an alert indicating that aperson left unattended bag on a subway station). In another embodiment,alerts are issued to indicate that normal events are taking place in thescene (e.g., an alert indicating that a car parked). The methodconcludes with step 275.

It should be noted that it is not necessary to perform all of theabove-described steps in the order named. Furthermore, not all of thedescribed steps are necessary for the described method to operate. Whichsteps should be used, in what order the steps should be performed, andwhether some steps should be repeated more often than other steps isdetermined, based on, for example, needs of a particular user, specificqualities of an observed environment, and so on.

FIGS. 3 through 6 illustrate different components of the computer visionengine 135 illustrated in FIG. 1, according to one embodiment of thepresent invention. Specifically, FIG. 3 illustrates components of abackground-foreground module 300. The background-foreground module 300uses features in each video frame to identify which pixels belong to abackground image and which belong to a foreground image. In oneembodiment, video frames are analyzed to classify each pixel asdisplaying part of the background image for the scene (and that frame)or displaying part of a foreground image for that frame.

Typically, pixels that do not change color over time are considered partof the background image. By sampling the color value of a pixel overtime, the presence of a foreground object in some frames may be washedout. Further, as the background image may be updated dynamically, thebackground image may compensate for changes in light and shadow.Similarly, pixels that change color, relative to the background image,are assumed to be displaying a foreground object. In other words, themotions of foreground objects in a scene are determined based ondifferences between pixel color values in successive the video frames.Generally, a background image may be envisioned as a video frame ofpixels having the foreground objects cut-out. Foreground images may beenvisioned as pixels that occlude the background. Alternatively, onlyone foreground image may be used. Such foreground image may beenvisioned as a transparent video frame with patches of the foregroundpixels. It should be noted, that while two consecutive frames may besufficient to track a given foreground object, comparing multipleconsecutive frames provides more accurate results when determining thebackground image for a given scene.

It should also be noted, that a pixel originally determined as abackground pixel (in one frame) may become a foreground pixel (inanother frame) and vice versa. For example, if the color value of apixel in the background begins to change, it may be appropriate tore-classify it as a foreground pixel (e.g., a car parked in a parkinglot for a long period of time starts moving). Similarly, a changingpixel might become static, thus it might be necessary to re-qualify suchpixel as a background pixel (e.g., a trash can has been brought to asubway station for permanent use). However, to avoid unnecessary pixelsre-classification and to improve interpretation of what is included inthe background and foreground images, in one embodiment, the behaviorrecognition system may classify pixels as being part of a short termbackground (STBG), short term foreground (STFG), long term background(LTBG), and long term foreground (LTFG). STBG and STFG are stored inmemory for a short period of time (e.g., seconds or less), while LTBGand LTFG are stored in memory for longer period of times (e.g.,minutes). Determining pixels to be STBG/STFG at first, and theninterpreting only the qualifying pixels as LTBG/LTFG allows for moreaccurate determination of which pixels are part of thebackground/foreground image. Of course, the time periods may be adjustedaccording to the events occurring within a particular scene.

FIG. 3 illustrates components of the background-foreground module 300that may be used to generate background and foreground images for avideo frame, according to one embodiment of the invention. Initially,video frames are received by a background training module 305. Thebackground-foreground module 300 may be trained using an initialsequence of frames. The training allows the background-foreground module300 to build a background image of the scene depicted in the acquiredvideo frames. The training process may occur during an initializationstage of the system; namely, before a background image of the scene hasbeen determined.

The dark scene compensation module 310 may process pixel values tocompensate for low or dark lighting conditions in portions of the scene.Additionally, the dark scene compensation module 310 may be configuredto provide the processed video frames to a STFG/STBG module 315 andLTBG/LTBG module 320. The STFG/STBG module 315 may be configured toidentify STFG and STBG pixels within a given frame and provide thisinformation to a stale FG module 325 and an illumination compensationmodule 335, respectively. The LTFG/LTBG module 320 may be configured toidentify LTFG and LTBG pixels and, similar to the STFG/STBG module 315,provide this information to the stale FG module 325 and illuminationcompensation module 335, respectively. The stale FG module 325identifies stale foreground pixels and provides the results to an updateBG module 330. A pixel may become “stale” when the BG/FG determinationis obsolescent and needs to be reassessed. Once received, theillumination compensation module 335 may dynamically adjust theprocessing for changes in lighting (e.g. the brightening/darkening of ascene due to clouds obscuring the sun, or adjustments to artificiallight sources), and the dark scene compensation module 310 willdynamically provide special processing in the limit of extremely darkregions and/or low-light conditions. The update BG module 330 updates abackground image model and transfers the results to the illuminationcompensation module 335, which in turn, after processing all thereceived results, provides the processed results to the LTFG/LTBGmodule.

Thus, collectively, the background-foreground module 300 determines aset of background and foreground images and/or background andforegrounds models for use by other components of the behaviorrecognition system. The background and foregrounds models distinguishbetween pixels that are part of scene background (i.e., part of thestage) and pixels that display foreground objects (i.e., elementsperforming some action on the stage). It should be noted that while inthe above description of the background-foreground module 300 thereferences are made to only one background image, alternatively, thebackground-foreground module 300 may employ multiple background images(e.g., the scene of the image frame might be divided in severalbackground zones for more accurate background identification).

In one embodiment, the background model/image may include additionalinformation, such as pixel colors. Further, the foreground model/imagetypically includes additional pixel characteristics, such as color.However, keeping or collecting such information might be omitted (e.g.,to save resources in an environment where knowing colors does notsignificantly improve distinguishing between objects of interest, forexample a conveyer line transporting objects of the mostly the same orsimilar color).

FIG. 4 illustrates a foreground object module 400 configured to identifyobjects displayed in the foreground images of a scene, according to oneembodiment of the invention. In general, the foreground object module400 may be configured to receive the foreground images produced by thebackground-foreground module 300 for a given frame, build/update searchmodels for the foreground images, and attempt to track motions of adisplayed object in the foreground images as that object moves about thescene from frame-to-frame.

As illustrated in FIG. 4, the foreground object module 400 includes ablob detection module 405, a build/update module 410, a tracking module420 and 1-M search models, search model 1 (415 ₁), search model 2 (415₂), through search model M(415 _(M)). In one embodiment, the blobdetection module 405 may be configured to analyze foreground images todetect groups of related pixels, referred to as the foreground blobs,where each such group of pixels is likely to represent a distinct objectwithin the scene. Additionally, each detected foreground blob isassigned a tracking identification number. The foreground blobs are usedby the build/update module 410 to build/update the search models 415₁-415 _(M), wherein already existing search models have been built orupdated for blobs identified in previous video frames. In oneembodiment, to update the search models 415 ₁-415 _(m), the build/updatemodule 410 also uses results generated by the tracking module 420. If acurrently detected blob has no respective search model, such searchmodel is built (created).

At any given moment, the foreground object module 400 may includemultiple search models, each representing a different foreground blob.The number of search models may depend on how many foreground blobs areidentified by the blob detection module 405 within a foreground image.In one embodiment, the search models may be configured with predictivecapabilities regarding what the foreground blobs may do in subsequentvideo frames. For example, the search model associated with a givenforeground blob may include an expected future position (and shape) ofthat blob based on a present position and kinematic data. Further, eachsearch model may also include a variety of information derived about agiven foreground blob (e.g., textures, colors, patterns, z-depthposition within a scene, size, rates of movement, kinematics and thelike).

Further, different types of search models may be used according to theprinciples of the present invention. As stated, a search model may beused by the tracking module 420 to predict, find, and track motions of acorresponding object from frame-to-frame. In one embodiment, anappearance model is used. The appearance model includes pixels used todisplay an object (e.g., where a frame displays a human in theforeground image, the appearance model would include mostly pixelsoutlining the human and pixels inside the outline). In anotherembodiment the search model is implemented as a feature-based model,where the feature-based model represents pixels within a rectangle, suchas a minimal bounding rectangle encompassing an object (e.g., where anobject is a human, the feature based model could include a boundingrectangle encompassing the human). Alternatively, the feature-basedmodel may include multiple bounding rectangles for a given object, suchas rectangles of minimally possible sizes, encompassing differentregions of that object (e.g., where the frame displays a human, thefeature based model for such object could include several rectangles ofminimum size where the rectangles encompass different regions of thehuman, such as arms, legs, head, and torso).

Which search model is used may depend, for example, on an environmentbeing observed, preferences of a user of behavior recognition system,and so on. For example, while the appearance model is likely to providemore precise tracking, the feature based model may save resources,where, for example, shapes of the tracked objects of interest are simple(e.g., a luggage conveyer belt).

As mentioned above, the tracking module 420 uses the search models 415to track motions of the corresponding objects depicted in a videosequence from frame-to-frame as such objects move about the scene. Thetracking module 420 takes a detected foreground blob of a current videoframe and seeks a search model of a previous video frame that providesthe closest match with the foreground blob. In one embodiment, for eachcurrently detected foreground blob, the tracking module 420 seeks asearch model 415 that a relative dimensional vectoring distance betweenthe search model and the foreground blob is global minimum. This way,the tracking module 420 may track the locations of each objectrepresented by one of the search models 415 from frame-to-frame. In oneembodiment, the tracking module 420 uses kinematic information acquiredbased on previous video frames to estimate locations of the search modelwithin the current video frame.

FIG. 5 illustrates an estimator/identifier module 500 of a computervision engine, according to one embodiment of the present invention.Generally, the estimator/identifier 500 receives foreground blobs andrespective search models and attempts to classify objects in a videoframe, as represented by the foreground blobs, as members of knowncategories (classes). In one embodiment, if an object of interest hasbeen identified then the estimator/identifier module 500 estimates theobject of interest's pose, location, and motion. Theestimator/identifier 500 is usually trained on numerous positive andnegative examples representing examples of a given class. Further,on-line training may be used to update the classifier dynamically whileanalyzing frame-by-frame video.

As shown, the estimator/identifier 500 includes a classifier 505, class1 (510 ₁) through class N (510 _(N)), and identifier 515. The classifier505 attempts to classify a foreground object as a member of one of theclasses, class 1 (510 ₁) through class N (520 _(N)). If successful,static data (e.g., size, color, and the like) and kinematic data (e.g.,speed, velocity, direction and the like) representative of theclassified object may also be determined over a period of time (e.g.,X-number of frames) by the identifier 515. For each identified object,the estimator/identifier 500 outputs raw context events containing theabove-described static and kinematic characteristics of the object ofinterest and known object observations containing static and kinematiccharacteristic of an average member of the class of the identifiedobject.

In one embodiment, the system employs four classifiers: human, vehicle,other, and unknown. Until a class of object of interest is determined,such object is treated as a member of class “unknown.” Each classcontains pose, static, and kinematics data regarding an average memberof the class. In one embodiment, such data are continuously updated asmore objects of interest are classified and identified and their pose,static, kinematics data is determined and collected. It should be notedthat, typically, the estimator/identifier 500 processes information inreal-time, on a frame-by-frame basis.

FIG. 6 illustrates a context processor 600 of a computer vision engine135, according to one embodiment of the present invention. Generally,the context processor 600 combines results received from othercomponents of the computer vision engine 135, such as thebackground-foreground module 300, foreground object module 400, and theestimator/identifier module 500, to create an annotated map of a scenecaptured in the video frames. In one embodiment, the scene is segmentedinto spatially separated regions which are sorted according to z-depthof the scene and optionally labeled as depicting naturally- or man-madeelements.

As shown, the context processor 600 may include a region segmenter 605for breaking the scene into smaller areas (regions), a region sequencer610 for defining relations between the regions (e.g., as beingcloser/further from a video capturing device relative to one another),and a scene mapper 615 for generating the annotated map. In oneembodiment, the context processor 600 uses information regarding motions(such as trajectories) and locations of the tracked objects of interestto generate the annotated map.

FIGS. 7 and 8 illustrate different components of the machine learningengine 140 illustrated in FIG. 1. Specifically, FIG. 7 illustratescomponents of a semantic analysis module 700 and FIG. 8 illustratescomponents of a perception module 800, according to one embodiment ofthe present invention. Generally, the semantic module 700 createssemantic representations (i.e., symbolic representations) of motions andactions of the tracked objects. The semantic representation provides aformal way to describe what is believed to be happening in the scenebased on motions of a particular tracked object (and ultimately, basedon changes in pixel-color values from frame-to-frame). A formal languagegrammar (e.g., nouns and verbs) is used to describe events in the scene(e.g., “car parks,” “person appears,” and the like).

Subsequently, the semantic representations are analyzed for recognizablepatterns and the results are provided to a perception module 800illustrated in FIG. 8. In one embodiment, the semantic module 700 alsobuilds a symbolic map of the scene, including different aspects of theevents taking place in the scene, such as symbolic representations oftrajectories of the objects in the scene. In one embodiment, thesymbolic map may also include a frequency distribution (e.g., dataregarding how often and where certain classes or kinds of objects arepresent in the scene).

As shown in FIG. 7, the semantic module 700 includes a sensory memory710, a latent semantic analysis module (LSA) 715, a primitive eventmodule 725, a phase space partitioning module 730, an incremental latentsemantic analysis module (iLSA) 735, and a formal language module 740.The sensory memory 710 acquires information provided for the semanticmodule 700 and stores this information for subsequent use by theprimitive event module 725 and the phase space partitioning module 730.In one embodiment, the sensory memory 710 identifies which informationshould be provided for further analysis to the primitive event module725 and the phase space partitioning module 730.

The primitive event detection module 725 may be configured to identifythe occurrence of primitive events (e.g., car stops, reverses direction,disappears, appears; person bends, falls; exchange, and the like) in thesensory memory 710. The primitive events typically reflect changes inkinematic characteristics of the tracked objects. Thus, once an objectis classified as being a “car,” the primitive event detection module 725may evaluate data regarding the car to identify different behavioralevents as they occur. In one embodiment, the primitive events arepre-defined (e.g., for a specific environment where the self-learningbehavior recognition system is used). In another embodiment, only someof the primitive events are pre-defined (e.g., park, turn, fall down),while other primitive events are learned over time (e.g., objects ofcertain class may be found in a specific spot of the scene).

The phase space partitioning module 730 determines information regardinggeometric position having velocity of the objects in the scene.Accordingly, the primitive event module 725 and phase space partitioningmodule 730 allows the semantic module 700 to analyze data in twodistinct ways. Based on the results of the primitive event module 725and phase space partitioning module 730, the LSA 715 and the iLSA 735build/update a model of the scene, where the model includes the objectsof interest.

LSA 715 is generally an initial training module of the semantic module700. LSA gathers data over a period of time until LSA 715 generatesresults of sufficient statistical weight. In other words, LSA 715 learnsbasic layout of the scene, while iLSA 735 incrementally updates such alayout. It should be noted that iLSA 735 is sufficiently flexible tohandle changes in patterns of behavior taking place in the scene. Theformal language learning module 740 uses the data generated by the iLSA735 to create the semantic representations (the symbolic representationof what is happening in the scene) and provides the semanticrepresentations to the perception module 800 for learning what thecreated semantic representations mean.

FIG. 8 illustrates a perception module of a machine learning engine,according to one embodiment of the invention. The perception module 800may be configured to process the results generated by at least some ofthe components of the computer vision 135 and the machine learningengine 140 (e.g., the estimator/identifier module 500, the contextprocessor 600, the semantic module 700, etc.). Generally, the perceptionmodule 800 learns patterns, generalizes based on observations, andlearns by making analogies.

As shown in FIG. 8, the perception module 800 may include a perceptiveassociative memory 805, a scheduler 810, a workspace 815, an episodicmemory 820, and a long-term memory 825. The workspace 815 provides amemory region that reflects what information is currently beingevaluated by machine learning engine 140. That is, the workspace 815stores what elements of data currently have the “attention” of themachine learning environment 140. As described below, the data in theworkspace 815 may include a collection of precepts (each describing anevent) and codelets (The perceptive associative memory 805 collects dataprovided to the perception module 800 and stores such data as percepts.Each percept may provide data describing something that occurred in thevideo, such as a primitive event. The perceptive associative memory 805provides percepts and/or codelets to the workspace 815.

A codelet provides a piece of executable code, which describes and/orlooks for relations between different percepts. In other words, acodelet summarizes rules for determining a specific behavior/event(e.g., parking event), where the behavior/event involves one or morepercepts. Each codelet may be configured to take a set of input preceptsand process them in a particular way. For example, a codelet may take aset of input percepts and evaluate them to determine whether aparticular event has occurred (e.g., a car parking) Using the example ofa car parking, the precept may update episodic memory 820 withinformation about which car, the color of the car, where the car parked,etc. Further, information about this detected primitive event may beused to update the definition of the primitive event in the long-termmemory 825. Further still, codelets recognizing anomalies are employedby the perception module 800. Such codelets access percepts and if acertain percept does not statistically correlate with previouslyaccumulated statistical data, an abnormal event may be identified.

In one embodiment, the codelets are fully pre-written. In anotherembodiment, at least some codelets are not fully pre-written, butinstead, generated over time. For example, a codelet describing normalbehavior for certain percept(s) may be self-generated/modifying based onaccumulated data describing corresponding observed events.

The scheduler 810 determines which codelet needs to be activated at anygiven time. For example, the scheduler 810 may seek to identify a matchbetween percepts placed in the workspace 815 and the codelets. When anappropriate set of inputs required for a given codelet (e.g., a set ofprecepts) is available that codelet may be placed in the workspace 815and invoked. When multiple codelets are available for activation, thedetermination of when and which codelet to activate may be random.However, in one embodiment, certain codelets configured have priorityover others (e.g., a codelet defining a certain abnormal behavior). Ateach given moment numerous codelets may be activated by the scheduler810 within the workspace 815.

The perception module 800 also uses the episodic memory 820 andlong-term memory 825 to capture both short-term and long-term dataregarding primitive events. The episodic memory 820 is a short termmemory for storing recent percepts. For example, a percept that has beenrecently changed is found in the episodic memory 820. Percepts areplaced into the episodic memory 820 from the workspace 815. At the sametime, the workspace 815 may use the percepts stored in the episodicmemory 820 to match them with the respective codelets.

Typically, at least some percepts migrate from the episodic memory 820to the long-term memory 825. However, not every piece of data placedinto the episodic memory 820 migrates to the long-term memory 825. Somedata decays from the episodic memory 820 without ever reaching thelong-term memory 825 (e.g., data describing a one-time event that hasnot been determined as abnormal).

At the same time, aspects of that event may be used to reinforceinformation in long-term memory 825 (e.g., aspects of how, where, andhow long a car parked in a parking space). Thus, long-term memory 825may be used to build and accumulate general patterns of behavior withina given scene. In one embodiment, the patterns of behavior stored in theepisodic memory 820 and the patterns of behavior that have acquiredsufficient statistical weight are moved to the long-term memory 825 asthe general patterns of behavior. However, not all data placed into thelong-term memory 825 stays there. Some data eventually decay (e.g.,specific details). For example, if several cars of different colors havebeen parked in the same place over a period of time, a general patternof a car being able to park in that specific place may be learned andplaced into the long-term memory 825. However details regardingpreviously parked cars, such as their colors, would decay from thelong-term memory 825 after some period of time.

In one embodiment, the workspace 815 uses the general patterns ofbehavior found in the long-term memory 825 to determine events takingplace in the scene. Once an event has been recognized, the informationindicating that the recognized event has been identified is generated.Such information is subsequently used to generate alerts. While in oneembodiment, only alerts regarding identified abnormal behavior areissued (e.g., assault), in another embodiment, alerts describingidentified normal are issued as well (e.g., car parked).

FIGS. 9A-9C illustrate a scenario taking place at a subway station 900where a behavior recognition system detects an abnormal behavior andissues an alert, according to one embodiment of the present invention.As shown, a stationary video camera 915 captures events taking place atthe subway station 900 and provides video images depicting the events tothe behavior recognition system. As illustrated in FIGS. 9A-9C, thevideo camera 915 captures video images of a man 905 carrying a bag 910while approaching the trash can 920 (FIG. 9A), putting the bag 910 downon the ground next to the trash can 920 (FIG. 9B), and leaving the bag910 behind (FIG. 9C). Based on the learning from observing humans enterthe subway station 900, the act of leaving an “other” object (i.e., thebag) brought by an object classified as a human may be identified asabnormal, and accordingly, the behavior recognition system may issue analert to indicate the occurrence of such an event.

According to the above discussed principles, the behavior recognitionsystem treats the pixels displaying stationary trash can 920 as a partof a background image, without specifically identifying the trash can920 as a trash can. In contrast, the behavior recognition system treatsboth the man 905 and the bag 910 as foreground image(s). Initially (FIG.9A), the self-learning behavior recognition system may consider the man905 and the bag 910 as one foreground blob. However, as the man 905 putsthe bag 910 down (FIGS. 9B-9C), the man and the bag 910 become parts ofseparate foreground blobs. While in one embodiment, as the man 905 picksup the bag 910 their respective foreground blobs would merge into a newforeground blobs, in another embodiment, the man 905 and the bag 910 arecontinued to be treated as two distinct foreground blobs. In yet anotherembodiment, the man 905 and the bag 910 are considered to be separateforeground blobs from the beginning (FIG. 9A).

For both the man 905 and the bag 910 the behavior recognition systembuilds and updates search models to track these objects frame-by-frame.Further, behavior-recognition system classifies the man 905 as a “human”and the bag 910 as “other” (alternatively as a “bag”), collectsinformation about them, and predicts their actions based on previouslylearned behavior of people and bags in the subway station. As leaving abag behind is not associated with a normal learned behavior, thebehavior-recognition system identifies such behavior as abnormal andissues an alert. Alternatively, such behavior may be identified asabnormal because the system has previously learned that the leaving abag behind situation indicates abnormal behavior.

While the foregoing is directed to embodiments of the present invention,other and further embodiments of the invention may be devised withoutdeparting from the basic scope thereof, and the scope thereof isdetermined by the claims that follow.

What is claimed is:
 1. A method for processing video image data, themethod comprising: identifying, by operation of one or more processors,one or more objects depicted in a sequence of video frames capturing ascene; generating, from the sequence of video frames, a plurality ofinformation streams characterizing one or more of the identifiedobjects; and generating, from the plurality of information streams, by amachine learning engine, one or more object classifications to assign toobjects depicted in the sequence of video frames, wherein the machinelearning engine is configured to derive patterns of behavior engaged inby objects having a common object classification and to identifyinstances of the patterns of behavior engaged in by objects depicted inthe sequence of video frames as said instances occur.
 2. The method ofclaim 1, wherein the plurality of information streams includes a streamcharacterizing kinematics of the at least one object, as depicted in thesequence of video frames.
 3. The method of claim 1, wherein theplurality of information streams includes a stream characterizing anappearance of the at least one object, as depicted in the sequence ofvideo frames.
 4. The method of claim 1, wherein the plurality ofinformation streams includes a stream specifying a feature basedclassification of the at least one object based on kinematics data andappearance data derived for the at least one object derived from thesequence of video frames.
 5. The method of claim 1, wherein identifyingone or more objects depicted in a sequence of video frames comprises:generating a background model of the scene; and segmenting one or moreforeground objects depicted in each of the sequence of video framesbased on the generated background model.
 6. The method of claim 5,further comprising, tracking one or more of the segmented foregroundobject across a plurality of the sequence of video frames, and whereinone of the plurality of information streams includes a trackedtrajectory of one or more of the segmented objects.
 7. The method ofclaim 1, further comprising issuing at least one alert indicating anoccurrence of one of the identified patterns of behavior engaged in byone of the identified objects.
 8. The method of claim 1, wherein themachine learning engine is further configured to determine, from theplurality of information streams, whether an occurrence of one of theidentified patterns of behavior is normal or anomalous event, relativeto prior analysis of the indentified objects depicted in the sequence ofvideo frames, as represented by of the plurality of information streamsgenerated from the sequence of video frames.
 9. A non-transitorycomputer-readable storage medium containing a program, which, whenexecuted on a processor is configured to perform an operation,comprising: identifying, by operation the processor, one or more objectsdepicted in a sequence of video frames capturing a scene; generating,from the sequence of video frames, a plurality of information streamscharacterizing one or more of the identified objects; generating, fromthe plurality of information streams, by a machine learning engine, oneor more object classifications to assign to objects depicted in thesequence of video frames, wherein the machine learning engine isconfigured to derive patterns of behavior engaged in by objects having acommon object classification and to identify instances of the patternsof behavior engaged in by objects depicted in the sequence of videoframes as said instances occur.
 10. The computer-readable storage mediumof claim 9, wherein the plurality of information streams includes astream characterizing kinematics of the at least one object, as depictedin the sequence of video frames.
 11. The computer-readable storagemedium of claim 9, wherein the plurality of information streams includesa stream characterizing an appearance of the at least one object, asdepicted in the sequence of video frames.
 12. The computer-readablestorage medium of claim 9, wherein the plurality of information streamsincludes a stream specifying a feature based classification of the atleast one object based on kinematics data and appearance data derivedfor the at least one object derived from the sequence of video frames.13. The computer-readable storage medium of claim 9, wherein identifyingone or more objects depicted in a sequence of video frames comprises:generating a background model of the scene; and segmenting one or moreforeground objects depicted in each of the sequence of video framesbased on the generated background model.
 14. The computer-readablestorage medium of claim 13, wherein the operation further comprises,tracking one or more of the segmented foreground object across aplurality of the sequence of video frames, and wherein one of theplurality of information streams includes a tracked trajectory of one ormore of the segmented objects.
 15. The computer-readable storage mediumof claim 9, wherein the operation further comprises issuing at least onealert indicating an occurrence of one of the identified patterns ofbehavior engaged in by one of the identified objects.
 16. Thecomputer-readable storage medium of claim 9, wherein the machinelearning engine is further configured to determine, from the pluralityof information streams, whether an occurrence of one of the identifiedpatterns of behavior is normal or anomalous event, relative to prioranalysis of the indentified objects depicted in the sequence of videoframes, as represented by of the plurality of information streamsgenerated from the sequence of video frames.
 17. A system, comprising: avideo input source; a processor; and a memory storing: a computer visionengine, wherein the computer vision engine is configured to: identify,by operation of one or more processors, one or more objects depicted ina sequence of video frames capturing a scene, and generate, from thesequence of video frames, a plurality of information streamscharacterizing one or more of the identified objects; and a machinelearning engine, wherein the machine learning engine is configured to:generate, from the plurality of information streams, one or more objectclassifications to assign to objects depicted in the sequence of videoframes, wherein the machine learning engine is configured to derivepatterns of behavior engaged in by objects having a common objectclassification and to identify instances of the patterns of behaviorengaged in by objects depicted in the sequence of video frames as saidinstances occur.
 18. The system of claim 17, wherein the plurality ofinformation streams includes a stream characterizing kinematics of theat least one object, as depicted in the sequence of video frames. 19.The system of claim 17, wherein the plurality of information streamsincludes a stream characterizing an appearance of the at least oneobject, as depicted in the sequence of video frames.
 20. The system ofclaim 17, wherein the plurality of information streams includes a streamspecifying a feature based classification of the at least one objectbased on kinematics data and appearance data derived for the at leastone object derived from the sequence of video frames.
 21. The system ofclaim 17, wherein identifying one or more objects depicted in a sequenceof video frames comprises: generating a background model of the scene;and segmenting one or more foreground objects depicted in each of thesequence of video frames based on the generated background model. 22.The system of claim 21, wherein the computer vision engine is furtherconfigured to track one or more of the segmented foreground objectacross a plurality of the sequence of video frames, and wherein one ofthe plurality of information streams includes a tracked trajectory ofone or more of the segmented objects.
 23. The system of claim 17,wherein the machine learning engine is further configured to issue atleast one alert indicating an occurrence of one of the identifiedpatterns of behavior engaged in by one of the identified objects. 24.The system of claim 17, wherein the machine learning engine is furtherconfigured to determine, from the plurality of information streams,whether an occurrence of one of the identified patterns of behavior isnormal or anomalous event, relative to prior analysis of the indentifiedobjects depicted in the sequence of video frames, as represented by ofthe plurality of information streams generated from the sequence ofvideo frames.